Reading City-Regions

Edited by
John R. Bryson, Lauren Andres and Rachel Mulhall

This Research Agenda provides both a state-of-the-art review of existing research on city-regions, and expands on new research approaches. Expert contributors from across the globe explore key areas for reading city-regions, including: trade, services and people, regional differentiation, big data, global production networks, governance and policy, and regional development. The book focuses on developing a more integrated and systematic approach to reading city-regions as part of regeneration economics, identifying conceptual and methodological developments in this field of study.

Edited by
Urban Gråsjö, Charlie Karlsson and Iréne Bernhard

Developed countries must be incredibly innovative to secure incomes and welfare so that they may successfully compete against international rivals. This book focuses on two specific but interrelated aspects of innovation by incumbent firms and entrepreneurs, the role of geography and of open innovation.

Åke E. Andersson and David Emanuel Andersson

In this chapter knowledge capital is seen as a specific combination of subsets of human and social capital, much as real estate capital combines physical and social capital. Knowledge capital is a key factor that drives economic growth and development. Knowledge is different from information; it is more complex and multifaceted, as it can be private or public. It can be embodied in machinery or tacit knowledge in humans, but dissemination processes cause its disembodiment. Scientific knowledge has become an increasingly important precondition for the emergence of investments in industrial research and development. The broad spectrum of new technologies in the pharmaceutical, biotechnological, information and transportation industries would have been unthinkable without earlier fundamental creativity in mathematics, physics, chemistry and biology. Scientific breakthroughs almost always occur many decades before being exploited by entrepreneurial innovators. Rogers Hollingsworth has shown that the increasing complexity of many products and production systems requires a reorganization of scientific research with a greater emphasis on multidisciplinary departments and laboratories. The possibility of exploiting advantages of a diversified scientific knowledge base also points toward increasing dynamic comparative advantages of locating universities and research institutes in large cities. Quantitative analyses of science networks show that the San Francisco Bay Area, Boston, London, Tokyo, Paris and Randstad (Amsterdam) are the most important nodes in the world of science, with Beijing, Seoul and Shanghai exhibiting the highest growth rates in science output among large cities. The advantages of dynamic interactions between scientific creativity and industrial development will reinforce the long-term sustainable growth in regions that host large-scale agglomerations of scientific research.

Åke E. Andersson and David Emanuel Andersson

In this chapter we claim that all goods are durable and in this respect they are all capital goods by definition. Our claim builds on Frank Knight’s analysis: any durable good is capital and must thus have a future return that is intrinsically uncertain. It is this uncertain return that is the subject matter of entrepreneurial judgment, as Knight also explained. But it is not only goods that are capital. Labor is also capital, which is why it makes more sense to speak of human capital. And this, too, reflects uncertain future returns, in this case of investments in education. Even land is capital, since inaccessible land without network links to the rest of the world yields no return: it lacks capital value. The durability of capital and the rate of depreciation are key attributes of capital. Deterministic models show that if households and firms were to optimize these attributes, then the equilibrium growth rate would equal the real interest rate in a multi-good economy. We also show that a good’s durability determines the number of firms and thus the potential market form, in the case that production, transport and transaction costs jointly determine equilibrium. The greater the durability of a good is, the smaller its trading cost will be, other things being equal. Trade will keep expanding until there are no price differences between different locations for the limiting case of extremely durable goods. For goods with extreme durability and low transportation and transaction costs, the law of one market price will thus prevail.

Åke E. Andersson and David Emanuel Andersson

Linear and linearized equations dominate economic and econometric modeling. The gains in terms of solubility and interpretation are considerable. However, important phenomena such as social interactions are then at best subject to informal treatments and at worst relegated to the class of supposedly irrelevant phenomena. This chapter explores the potential of non-linear dynamic theories and models of growth and development. We discuss examples of non-linear phenomena that are amenable to formal analysis such as the spread of infectious diseases, segregation processes and imitative behavior. Multiple equilibria occur frequently in non-linear models. One example is the Kaldor model of business cycles; another is Mees’s model of urban growth and decline. Analyzing the stability of models is central to the study of economic dynamics, where the dynamic stability of an equilibrium requires negative feedback. From the 1960s onward, structural stability became a recurrent catchphrase in dynamic analyses. In this chapter we claim that structural instability is a precondition for creativity in the development of new ideas in science and the arts. It is generally only possible to achieve a new stable equilibrium in conjunction with the completion of a creative process. Realistic dynamic economic models tend to contain a mixture of positive and negative feedback loops, leading to chaotic outcomes. One consequence is deterministic and stochastic processes become indistinguishable in practice. It is for this reason that Benoit Mandelbrot claimed that most “financial analyses” are spurious applications of stochastic theory to situations where fractal models would yield better pattern predictions.

Åke E. Andersson and David Emanuel Andersson

Expectations of the future as influenced by a recent time series of returns tend to have the greatest influence on the value of capital. Most models of capital valuation employ the assumption that investors in capital make decisions that include a trade-off between risk (also known as “probabilistic uncertainty”) and expected returns. The so-called “CAPM” and “APT” models that we discuss in this chapter posit that investors optimize their portfolios of capital by minimizing risk subject to some required expected return. These procedures are applicable in situations of probabilistic uncertainty where the securities and other assets have a well-documented and uneventful history and other transparent characteristics. However, measures of statistical risk are no more than guesswork in the structurally uncertain situations that are associated with new start-ups and firms that specialize in disruptive product innovations. Uncertainty in this Knightian sense implies a set of possible future outcomes that is open-ended; there is no way to know how many possible outcomes should be listed as feasible. There is thus no structure that allows investors to make use of subjective probabilities in any meaningful sense. Optimism bias is common in situations of structural uncertainty, according to numerous empirical studies. This bias is particularly common when the stakes are high, such as when investment decisions involve start-ups or mergers and acquisitions. In this chapter we also discuss different theories of entrepreneurship before arriving at our conclusion, which is that Frank Knight’s entrepreneurship theory—with ownership, uncertainty and judgment as catchwords—is especially useful as a theoretical foundation for understanding dynamic markets. It is possible to extend Knightian theory in various directions, for example by integrating processes of adaptation and learning along the lines of Brian Arthur’s work. The aggregation of capital into a macro entity is another problem that we address in this chapter. We conclude that the only logical way of measuring macroeconomic capital is using the expectation-derived valuations of firms that stock markets and real estate markets continuously provide to market participants.

Åke E. Andersson and David Emanuel Andersson

General equilibrium theory has been a dominant mode of thinking among economists since the mid-nineteenth century. In this chapter we claim that the solutions to the equations of deterministic as well as stochastic general equilibrium models are highly dependent on the structure of the infrastructural arena. In short, with the wrong institutions, insufficient networks for communications and transport or insufficient public knowledge and information, competitive equilibria cannot be found. Inclusion of the infrastructure—material networks, public knowledge and institutions—is a prerequisite for realistic theories, irrespective of whether these theories concern the growth of capital, competitive market equilibria or interactions between information flows and market processes. A fatal flaw in the dominant theory is the absence of institutions, especially the absence of property rights structures. Such institutions are necessary for the determination of initial wealth and income distributions as well as for the creation of mechanisms that ensure the stability and uniqueness of price-setting processes. To solve these problems we propose a general theory of the impacts of the material and non-material infrastructures on short-term market equilibria and information flows and on the accumulation of private capital stocks. This theory also allows for the possibility of occasional bifurcations into new economic structures.

Åke E. Andersson and David Emanuel Andersson

In this chapter we provide a broad overview of the four logistical revolutions that initially only shaped European economic history, but later have come to affect all of the world’s regions. The First Logistical Revolution was a consequence of a slow but persistent extension of transport and communication networks, which eventually resulted in a sudden phase transition and a network that encompassed most of Western Europe. This led to a dramatic spatial restructuring of the European economy and the establishment of hundreds of market towns. While changes to the transport network was the underlying cause of the First Logistical Revolution, later restructurings also depended on other factors that improved network connectivity, especially technological knowledge gains and institutional reforms. The development of reliable credit systems was a major cause of the rapid increase in long-distance trade and commerce in the seventeenth century. Influential decision makers in Amsterdam seized the opportunity to innovate a series of path-breaking financial institutions and organizations in the early seventeenth century. The establishment of the first stock exchange was followed by the first bank with public guarantees. But Amsterdam did not remain unique for long. By the end of the seventeenth century, the Bank of England offered the same services but on a larger scale. The Bank of England had the right to organize transactions involving both money and bills of exchange; it later became the prototype for central banks in all parts of the world. The City of London thereby established its position as a leading financial center, which it has remained ever since. The Third Logistical Revolution is more often called the Industrial Revolution; technological progress was however only a proximate cause of this phase transition. The underlying cause was the abandonment of centrally planned mercantilism, first in Britain and later elsewhere, and the rise of the sort of liberal institutions that are closely associated with Scottish Enlightenment figures such as David Hume and Adam Smith. These institutions included free international trade, unrestricted entry to product markets and the reliable enforcement of private property rights with the help of an independent legal system such as English common law. We are now experiencing a Fourth Logistical Revolution, which is sometimes referred to as the Information Revolution. This ongoing restructuring is primarily a consequence of technological advances in information and telecommunications, which in turn reflect much earlier creative breakthroughs in science, as exemplified by the theoretical contributions of Alan Turing and John von Neumann in the 1930s and 1940s. The main contemporary symptoms are especially rapid global growth rates of international trade in services, science output and patents. In advanced regions of the world, there has been a transformation of the occupational structure away from manual work in the direction of creative knowledge services.